Robust Rearrangement Planning Using Nonprehensile Interaction

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Abstract

As we work to move robots out of factories and into human environments, we must empower robots to interact freely in unstructured, cluttered spaces. Humans do this easily, using diverse, whole-arm, nonprehensile actions such as pushing or pulling in everyday tasks. These interaction strategies make difficult tasks easier and impossible tasks possible. In this thesis, we aim to enable robots with similar capabilities. In particular, we formulate methods for planning robust open-loop trajectories that solve the rearrangement planning problem using nonprehensile interactions. In these problems, a robot must plan in a cluttered environment, reasoning about moving multiple objects in order to achieve a goal. The problem is difficult because we must plan in continuous, high-dimensional state and action spaces. Additionally, during planning we must respect the physical constraints induced by the nonprehensile interaction between the robot and the objects in the scene. Our key insight is that by embedding physics models directly into our planners we can naturally produce solutions that use nonprehensile interactions such as pushing. This also allows us to easily generate plans that exhibit full arm manipulation and simultaneous object interaction without the need for programmer defined high-level primitives that specifically encode this interaction. We show that by generating these diverse actions, we are able to find solutions for motion planning problems in highly cluttered, unstructured environments. In the first part of this thesis we formulate the rearrangement planning problem as a classical motion planning problem. We show that we can embed physics simulators into randomized planners. We propose methods for reducing the search space and speeding planning time in order to make the planners useful in real-world scenarios. The second part of the thesis tackles the imperfect and imprecise worlds that reflect the true reality for robots working in human environments. We pose the rearrangement planning under uncertainty problem as an instance of conformant probabilistic planning and offer methods for solving the problem. We demonstrate the effectiveness of our algorithms on two platforms: the home care robot HERB and the NASA rover K-Rex. We demonstrate expanded autonomous capability on HERB, allowing him to work better in high clutter, completing previously infeasible tasks and speeding feasible task execution. In addition, we show these planners increase autonomy for the NASA rover K-Rex by allowing the rover to actively interact with the environment.